graph neural networks

CRISP: Category-Role Informed Sheaf Propagation Network

Dynamic Spatial-Temporal Mapping for EEG-to-fMRI Synthesis via Gaussian-Guided Heterogeneous Graph Neural Network

Graph neural networks versus feature-only learners for Chinese A-share sanction prediction: a holder–manager graph without transaction edges — reproducibility archive

Reproducibility companion to Graph neural networks versus feature-only learners for Chinese A-share sanction prediction: a holder–manager graph without transaction edges. Contains source, fixed-fit aggregates, source documentation, and tests for table regeneration. The reconstructed common-cutoff endpoint and graph sem...

Weakly-Supervised Temporal Decomposition with Graph Neural Networks for Parkinsonian Turning Assessment

Multi-scale Dual-Domain Fusion-Based Spatio-Temporal Graph Learning for fNIRS Signal Classification

Graph attention spatio-temporal graph neural network based human–light interaction modeling for indoor lighting environments

In indoor light environment design, the dynamic interaction between human behavior and lighting systems has an important impact on environmental perception results. However, existing methods are difficult to describe its spatial structure and temporal evolution characteristics at the same time. To this end, this paper...

Artificial Intelligence for Power, Performance, and Area Optimization in Digital Circuit Design

Digital circuit design becomes more difficult as electronic systems continue to increase incomplexity. Traditional Electronic Design Automation (EDA) methods use deterministic algorithmsand heuristic transformations to synthesize and optimize logic circuits, but large designs createincreasingly difficult search spaces....

Beyond Searching a Village: Learning to Recommend Diverse and Successful Collaborative Teams

Team recommendation involves selecting skilled experts to form an almost surely successful collaborative team, or refining the team composition to maintain or excel at performance. To address the tedious and error-prone manual process, computational approaches have been proposed, especially for web-scale social network...

Topological Proof Networks and Interdisciplinary Structural Isomorphisms: A Bourbaki 2.0 Framework for Categorical Graph Functor Closure and Discrete Metric Verification

Modern theoretical physics, pure mathematics, and artificial intelligence have converged upon a dual epistemological crisis: while automated neural theorem engines generate sprawling, opaque derivation steps that suffer from an unbridged "epistemic justification gap" (Tanswell & Berg, M\times\Phi 2026), human mathemati...

Messages Passed Along the Edges: A Contemporary Synthesis Review of Graph Neural Networks for Relational Data

This article presents a narrative review of Graph Neural Networks for Relational Data in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive...

A Citation Recommendation Algorithm Based on SciBERT and a Temporal-Aware Graph Attention Network

Citation recommendation plays a critical role in scholarly information retrieval by assisting researchers in identifying relevant and influential literature. Existing approaches typically rely on either textual semantic modeling or graph-based citation analysis, but often fail to jointly capture semantic relevance, str...

PSEALP: patch-based structural external attention with node memory for dynamic link prediction

Dynamic link prediction on temporal graphs is fundamental to many applications such as recommendation, knowledge base completion, and user–item interaction modeling. Most existing dynamic graph neural networks (DGNNs), including memory-based and attention-based models, operate on node-level embeddings and local tempora...